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Capturing Vortex Dynamics to Predict Acoustic Response using Machine Learning

Abstract Details

2019, Master of Science, Ohio State University, Computer Science and Engineering.
Deep learning has become a ubiquitous technology applied widely in scientific as well as non-scientific data domains, alike. Even though, they have shown significant improvements over conventional techniques in various fields of application such as computer vision, not much success has been achieved in dynamic physical systems like fluid flows. One of the prospective domain for application of deep learning techniques is prediction of jet noise that is an active area of research. A significant reason for jet noise is the intermittent events in the acoustic field generated due to interactions between the coherent structures in the flow field. This work introduces a machine learning based technique that extracts temporal sequence of vortex activity and predicts its acoustic emission. Sequence to sequence learning LSTM model is used for the prediction. Vortex tracking mechanism is also developed as part of this work, that traces a vortex through space and time, based on its high vorticity region. The technique is tested on high-fidelity simulation dataset of a Mach 1.3 perfectly expanded jet. The results indicate that the model is able to correlate the variation in vorticity and associated acoustic signals of a vortex enabling approximation of acoustic pattern for unseen vortices.
Srinivasan Parthasarathy, Dr. (Advisor)
Spyros Blanas, Dr. (Committee Member)
61 p.

Recommended Citations

Citations

  • Nair, A. (2019). Capturing Vortex Dynamics to Predict Acoustic Response using Machine Learning [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1546427424013197

    APA Style (7th edition)

  • Nair, Ashwati. Capturing Vortex Dynamics to Predict Acoustic Response using Machine Learning. 2019. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1546427424013197.

    MLA Style (8th edition)

  • Nair, Ashwati. "Capturing Vortex Dynamics to Predict Acoustic Response using Machine Learning." Master's thesis, Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1546427424013197

    Chicago Manual of Style (17th edition)